Tech Giants Slash AI Spending as ROI Questions Mount: Macro Watch
Major technology companies are scaling back artificial intelligence infrastructure investments as energy costs spiral and returns on massive capital commitments remain elusive. The pullback signals a fundamental repricing of AI's economic value and threatens to reshape cloud computing markets across North America and Europe.
Nvidia's data centre revenue growth decelerated to 126 percent year-over-year in the latest quarter, down from 217 percent the prior year, as major cloud providers extended purchasing cycles and questioned spending on unproven AI applications. The slowdown has already cut $200 billion from tech market valuations since September and is forcing a reckoning with the assumption that AI infrastructure spending would remain exponential indefinitely.
• Nvidia's data centre revenue growth rate fell from 217% YoY to 126% YoY—a 91 percentage-point deceleration in twelve months
The AI investment boom that began in late 2022 rested on a simple premise: train larger models, deploy faster, capture market share before competitors. Amazon, Google, Microsoft and Meta committed hundreds of billions to this race, treating data centre buildouts as infrastructure plays that would pay dividends for decades. By mid-2024, the narrative had hardened into orthodoxy. Analysts built models assuming capex would exceed $300 billion cumulatively through 2027. Strategic plans locked in accelerating commitments. Then the economics stopped working.
Energy costs emerged as the binding constraint. A single training run for an advanced language model now consumes 700,000 megawatt-hours of electricity—equivalent to what 215,000 US homes use annually. At regional power rates of $50–150 per megawatt-hour, training expenses alone exceed $35 million per model. Cooling and hardware amortization double that figure. Meanwhile, actual business returns from generative AI remained stubbornly modest: enterprise adoption stalled, consumer willingness to pay for AI features proved limited, and internal use cases often delivered lower productivity gains than initial pilots suggested.
The Spending Pullback and ROI Reality
The pullback is now visible across every major technology firm. Meta cut planned data centre expansions by 15 percent in January, citing "infrastructure efficiency" goals. Amazon decelerated its AWS AI capex guidance and signalled "measured" spending for 2025. Google extended the timeline for its Gemini model rollout and delayed new data centre activation. Microsoft's capex growth moderated to 28 percent—still substantial, but a marked slowdown from the 50 percent growth rates of 2023.
"The industry is confronting the gap between theoretical performance improvements and actual commercial value," says Kai-Fu Lee, former head of Google China and founding director of the Sinovation Ventures AI fund. "Enterprises spent 2024 experimenting with these tools. Most discovered that the productivity multiplier is closer to 1.2x than 5x. When you model that against infrastructure costs running $50,000 to $100,000 per unit of compute capacity per year, the math becomes brutal."
The counter-narrative comes from institutions still bullish on AI capex trajectories. Morgan Stanley's equity research team maintains that current spending reflects "prudent optionality" rather than fundamental doubt, and argues that capex will accelerate again once workload monetization improves. IMF economists similarly project that AI capex as a percentage of developed-market GDP will reach 1.2 percent by 2027, implying continued growth. These forecasts assume that current deployment challenges are temporary—engineering problems, not economic ones.
That framing increasingly looks disconnected from boardroom behaviour. When Microsoft's Satya Nadella publicly acknowledged that AI's "productivity impact is still being validated," the market heard what executives have stopped saying in earnings calls: we don't know if this spending pays off.
